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Higher Order Neural Networks for Stock Index Modeling

Higher Order Neural Networks for Stock Index Modeling
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Author(s): Yuehui Chen (University of Jinan, China), Peng Wu (University of Jinan, China) and Qiang Wu (University of Jinan, China)
Copyright: 2009
Pages: 19
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch006


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Artificial Neural Networks (ANNs) have become very important in making stock market predictions. Much research on the applications of ANNs has proven their advantages over statistical and other methods. In order to identify the main benefits and limitations of previous methods in ANNs applications, a comparative analysis of selected applications is conducted. It can be concluded from analysis that ANNs and HONNs are most implemented in forecasting stock prices and stock modeling. The aim of this chapter is to study higher order artificial neural networks for stock index modeling problems. New network architectures and their corresponding training algorithms are discussed. These structures demonstrate their processing capabilities over traditional ANNs architectures with a reduction in the number of processing elements. In this chapter, the performance of classical neural networks and higher order neural networks for stock index forecasting is evaluated. We will highlight a novel slide-window method for data forecasting. With each slide of the observed data, the model can adjusts the variable dynamically. Simulation results show the feasibility and effectiveness of the proposed methods.

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